name: premortem description: "Cognitive immune system for AI agents. Predicts and prevents failures BEFORE they happen using adversarial pre-execution reasoning. Zero dependencies, zero cost, universal quality improvement." version: 1.0.0 author: J. DeVere Cooley metadata: openclaw: emoji: "🛡️" always: true homepage: "https://github.com/jcools1977/Opengnaw" os: ["macos", "linux", "windows"]

Premortem — Cognitive Immune System for AI Agents

"The best way to avoid failure is to imagine you've already failed, then work backward." — Adapted from Gary Klein's premortem analysis

What This Skill Does

Premortem is a zero-cost reasoning enhancement that makes every agent action better. Before committing to any significant response, code change, or action, the agent runs a rapid internal "premortem" — it imagines the action has already failed, identifies the most likely causes of failure, and patches them before executing.

This is not a tool. This is not an API. This is a thinking pattern — a cognitive reflex that fires automatically, improving the quality of everything the agent does without any external dependencies.

When to Activate

Run a premortem pass before ANY of these actions:

Writing or modifying code (especially multi-file changes)
Executing destructive or irreversible commands
Providing architectural recommendations
Answering complex or ambiguous questions
Making plans with multiple steps
Generating content the user will publish or share
Taking actions that affect shared systems (git push, deployments, API calls)

Do NOT premortem trivial actions (reading files, listing directories, simple acknowledgments).

The Premortem Protocol

Phase 1: Snapshot the Intent

Before acting, crystallize what success looks like in one sentence:

INTENT: [What am I trying to achieve for the user?]

Phase 2: Fast-Forward to Failure

Imagine the action has been taken and it FAILED. Generate the top 3 most likely failure modes:

FAILURE MODE 1: [What went wrong?]
FAILURE MODE 2: [What went wrong?]
FAILURE MODE 3: [What went wrong?]

Use these failure lenses to probe different dimensions:

Lens	What to Check
Correctness	Is the output factually/logically wrong? Am I hallucinating?
Completeness	Am I missing edge cases, error handling, or requirements?
Intent Drift	Have I drifted from what the user actually asked for?
Side Effects	Will this break something else? Unintended consequences?
Assumptions	What am I assuming that might not be true?
Overengineering	Am I adding complexity the user didn't ask for?
Security	Does this introduce vulnerabilities (injection, exposure, etc.)?
Reversibility	Can the user undo this if it's wrong?

Phase 3: Inoculate

For each identified failure mode, apply a fix BEFORE executing:

PATCH 1: [How I'm preventing failure mode 1]
PATCH 2: [How I'm preventing failure mode 2]
PATCH 3: [How I'm preventing failure mode 3]

Phase 4: Execute with Confidence

Now take the action, incorporating all patches. The output is cleaner, safer, and more aligned with user intent.

Calibration Rules

Intensity Scaling

Not every action needs the same depth of premortem. Scale intensity to stakes:

Stakes Level	Premortem Depth	Time Budget	Example
Low	1 failure mode, mental only	~1 second	Answering a factual question
Medium	2-3 failure modes, brief	~3 seconds	Writing a function, making a plan
High	3+ failure modes, thorough	~5 seconds	Multi-file refactor, deployment, architecture
Critical	Full protocol + user confirmation	~10 seconds	Destructive commands, production changes

The Silent Rule

Premortems are internal reasoning. Do NOT narrate the premortem process to the user unless:

They explicitly ask about your reasoning
You discover a critical risk that requires user input before proceeding
The premortem reveals the user's request itself may be misguided

Anti-Paralysis Guard

A premortem must NEVER cause analysis paralysis. Constraints:

Maximum 3 failure modes per action (focus on the most likely, not every possibility)
If no significant failure modes are found in 2 seconds, proceed immediately
The premortem should take less than 10% of total action time
When in doubt, act and iterate rather than deliberate endlessly

Pattern Library: Common Failure Modes by Domain

Code Generation

Failure Mode	Premortem Check
Off-by-one errors	Verify loop bounds and array indices mentally
Missing null checks	Trace data flow from input to usage
Breaking existing tests	Consider what existing code depends on your changes
Wrong abstraction level	Ask: "Would a junior dev understand this immediately?"
Ignoring error paths	Ask: "What happens when this fails at runtime?"

Research & Answers

Failure Mode	Premortem Check
Hallucinated facts	Ask: "Can I point to where I learned this?"
Outdated information	Check: "Is this time-sensitive? Am I current?"
Missing nuance	Ask: "Am I oversimplifying? Is there a 'but...'?"
Confidence without evidence	Ask: "How sure am I, really? 60%? 90%?"
Answering the wrong question	Re-read the user's actual words, not your interpretation

System Actions

Failure Mode	Premortem Check
Data loss	Ask: "Is this reversible? Should I back up first?"
Permission escalation	Check: "Am I doing more than I was asked to?"
Blast radius	Ask: "What else does this affect beyond the target?"
Race conditions	Ask: "Could something change between my check and my action?"
Incomplete rollback	Ask: "If this fails halfway, what state am I in?"

Planning & Architecture

Failure Mode	Premortem Check
Scope creep	Ask: "Am I solving the stated problem or an imagined one?"
Missing constraints	Ask: "What hasn't the user told me that I need to know?"
Premature optimization	Ask: "Is the simple version good enough?"
Integration blindness	Ask: "How does this connect to what already exists?"
Single point of failure	Ask: "What happens if any one component fails?"

The Inversion Principle

The deepest power of the premortem comes from inversion — instead of asking "How do I make this good?", ask "How could this go terribly wrong?"

Inversion catches failure modes that positive-framing misses because:

Positive framing has a confirmation bias toward your current approach
Negative framing activates different reasoning pathways
Humans (and LLMs) are better at spotting problems than confirming correctness

Inversion in Practice

Instead of: "Let me write clean code" → Ask: "What would make this code a nightmare to maintain?" → Avoid those patterns.

Instead of: "Let me give a helpful answer" → Ask: "What would make this answer actively harmful?" → Don't do that.

Instead of: "Let me make a good plan" → Ask: "How would this plan fall apart?" → Reinforce those weak points.

Compound Effects

The premortem skill creates compounding quality gains over a conversation:

First action: Catches the obvious failure mode
Subsequent actions: Previous premortems create context awareness
Complex tasks: The agent develops a "failure intuition" that gets sharper
Long conversations: Prevents the gradual quality decay that plagues extended sessions

Integration with Other Skills

Premortem enhances every other skill the agent has:

Coding skills: Code is cleaner, fewer bugs
Research skills: Answers are more accurate, better calibrated
Automation skills: Actions are safer, more reversible
Communication skills: Responses are more aligned with user intent

The premortem is a multiplier, not an addition. It makes everything else better.

Examples

Example 1: Code Change Premortem

User asks: "Add pagination to the user list API"

INTENT: Add pagination support to the existing /users endpoint

FAILURE MODE 1: Breaking existing clients that expect the full list
FAILURE MODE 2: Off-by-one error on page boundaries
FAILURE MODE 3: No handling for page number beyond total pages

PATCH 1: Make pagination optional — default behavior returns all users (backward compatible)
PATCH 2: Use offset-based math and verify with edge case: exactly N items for page size N
PATCH 3: Return empty array (not error) for out-of-range pages, include total count in response

Example 2: Research Premortem

User asks: "What's the best database for my real-time chat app?"

INTENT: Recommend an appropriate database technology for real-time chat

FAILURE MODE 1: Recommending based on popularity rather than actual requirements
FAILURE MODE 2: Not asking about scale, team expertise, or existing infrastructure
FAILURE MODE 3: Giving a single recommendation without trade-offs

PATCH 1: Ask about message volume, latency requirements, and read/write patterns first
PATCH 2: Consider what the team already knows and what they're already running
PATCH 3: Present 2-3 options with clear trade-offs rather than a single "best"

Example 3: Destructive Action Premortem

User asks: "Delete all the test files that aren't being used"

INTENT: Remove unused test files to clean up the repository

FAILURE MODE 1: Deleting test files that ARE used but I failed to trace all references
FAILURE MODE 2: Removing tests that serve as documentation for complex behavior
FAILURE MODE 3: No way to recover if I'm wrong about what's "unused"

PATCH 1: Use grep to verify zero imports/references before marking as unused
PATCH 2: Flag tests with descriptive names and ask user before removing
PATCH 3: List all candidates and let user confirm before any deletion

Premortem

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